Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Organizing and classifying complex relational data can provide useful information for comprehending the structure of large integrated systems. For example, community-detection algorithms may be used to identify the presence of multiple sub-structures within a larger network and to categorize respective nodes of a network into one of the multiple sub-structures. Herein, such sub-structures within a network may be referred to as modules. Accordingly, a module may generally be understood to include a group of nodes in a network. For example, many networks include groups of nodes that are more densely connected internally than with the rest of a network.
While categorizing nodes into modules may be a good starting point, many complex networks contain relationships that are deeper than two levels. For instance, biological and social systems are often characterized by a hierarchical organization having multiple levels of submodules that are nested within modules. Network theory offers tools for identifying nested modules within a network of interconnected nodes, but current techniques for automatically ranking and mapping network structures tend to be inefficient, incomplete, inaccurate, time-consuming, and dependent on direct human-mediation.
Improvements are therefore desired.